A system and method for decision making for autonomous vehicles. The method includes determining if a decision scenario is present; generating a first random number; communicating the first random number to a receiver via visible light communication; receiving a second random number and determining a priority order based on the generated random numbers. The priority is communicated to all relevant units to determine the order in which the vehicles should proceed. An optical random generator may be used to generate the random number associated with each vehicle.

A method and system provide the ability to autonomously operate an unmanned aerial system (UAS) over long durations of time. The UAS vehicle autonomously takes off from a take-off landing-charging station and autonomously executes a mission. The mission includes data acquisition instructions in a defined observation area. Upon mission completion, the UAS autonomously travels to a target landing-charging station and performs an autonomous precision landing on the target landing-charging station. The UAS autonomously re-charges via the target landing-charging station. Once re-charged, the UAS is ready to execute a next sortie. When landed, the UAS autonomously transmits mission data to the landing-charging station for in situ or cloud-based data processing.

A ship body control device is provided, which includes a control lever having a neutral position, and processing circuitry. When the control lever is moved during an automatic cruise, the processing circuitry executes a deceleration control from a speed of a ship body during the automatic cruise, and when the position of the control lever is in agreement with a position for changing to a manual cruise, the processing circuitry changes the automatic cruise to the manual cruise at a rotating speed of an engine according to the position for changing to the manual cruise.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

A vehicle may include an authority tracker to grant and/or limit authority to provide guidance to the vehicle. The authority tracker may store a state in a memory identifying an actor that currently has the authority. An actor requesting to have authority to provide guidance to the vehicle may transmit a control message to the authority tracker which may, in turn, authorize or deny the request based on a policy. The authority tracker may periodically, and/or in response to a request, broadcast a state message identifying the actor that currently has authority, as recorded in the memory.

A method and system provide the ability to autonomously operate an unmanned aerial system (UAS) over long durations of time. The UAS vehicle autonomously takes off from a take-off landing-charging station and autonomously executes a mission. The mission includes data acquisition instructions in a defined observation area. Upon mission completion, the UAS autonomously travels to a target landing-charging station and performs an autonomous precision landing on the target landing-charging station. The UAS autonomously re-charges via the target landing-charging station. Once re-charged, the UAS is ready to execute a next sortie. When landed, the UAS autonomously transmits mission data to the landing-charging station for in situ or cloud-based data processing.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.